from vnpy.app.cta_strategy import (
    CtaTemplate,
    StopOrder,
    TickData,
    BarData,
    TradeData,
    OrderData,
    Direction,
    Offset,
    Status,
)
from vnpy.trader.constant import Exchange, Product
from vnpy.trader.utility import round_to

class CommodityOptionArbitrageStrategy(CtaTemplate):
    """
    商品期权无风险套利策略

    套利原理：
    1. 期权定价模型（如Black-Scholes模型）给出了期权的理论价格。
    2. 当实际市场价格偏离理论价格时，可能出现套利机会。
    3. 主要套利方式包括：
       a) 跨式套利：同时买入看涨和看跌期权
       b) 蝶式套利：同时操作不同行权价的期权
       c) 期现套利：期权与标的物之间的套利
    4. 考虑交易成本、流动性等实际因素，只有当价格偏离足够大时才进行交易。

    策略特点：
    1. 考虑交易冲击成本
    2. 实现订单管理，处理未成交和部分成交情况
    3. 考虑行权交割等操作细节
    """

    # 策略参数
    option_symbol = ""  # 期权合约代码
    underlying_symbol = ""  # 标的物合约代码
    price_diff_threshold = 0.01  # 价格差异阈值
    max_pos = 10  # 最大持仓
    risk_free_rate = 0.03  # 无风险利率

    # 变量
    option_pos = 0
    underlying_pos = 0

    def __init__(self, cta_engine, strategy_name, vt_symbol, setting):
        super().__init__(cta_engine, strategy_name, vt_symbol, setting)
        
        self.option_exchange = Exchange.SHFE  # 假设在上期所交易
        self.underlying_exchange = Exchange.SHFE

        self.option_orderid = ""
        self.underlying_orderid = ""

    def on_init(self):
        """
        策略初始化
        """
        self.write_log("策略初始化")
        self.load_bar(10)  # 加载10天的历史数据用于计算

    def on_start(self):
        """
        策略启动
        """
        self.write_log("策略启动")

    def on_stop(self):
        """
        策略停止
        """
        self.write_log("策略停止")

    def on_tick(self, tick: TickData):
        """
        Tick数据更新
        """
        if tick.vt_symbol == self.option_symbol:
            self.process_option_tick(tick)
        elif tick.vt_symbol == self.underlying_symbol:
            self.process_underlying_tick(tick)

    def process_option_tick(self, tick: TickData):
        """
        处理期权Tick数据
        """
        # 计算期权理论价格（这里需要实现具体的定价模型）
        theoretical_price = self.calculate_option_price(tick)
        
        # 检查是否存在套利机会
        if abs(tick.last_price - theoretical_price) > self.price_diff_threshold:
            if self.option_pos < self.max_pos:
                # 买入期权
                price = tick.ask_price_1 + self.price_diff_threshold  # 考虑交易冲击
                volume = self.max_pos - self.option_pos
                self.option_orderid = self.buy(self.option_symbol, price, volume, self.option_exchange)
            elif self.option_pos > -self.max_pos:
                # 卖出期权
                price = tick.bid_price_1 - self.price_diff_threshold  # 考虑交易冲击
                volume = self.option_pos + self.max_pos
                self.option_orderid = self.short(self.option_symbol, price, volume, self.option_exchange)

    def process_underlying_tick(self, tick: TickData):
        """
        处理标的物Tick数据
        """
        # 这里可以实现与标的物相关的套利逻辑
        pass

    def on_order(self, order: OrderData):
        """
        订单更新
        """
        if order.vt_orderid == self.option_orderid:
            if order.status == Status.ALLTRADED:
                self.option_pos += order.volume if order.direction == Direction.LONG else -order.volume
            elif order.status in [Status.REJECTED, Status.CANCELLED]:
                self.write_log(f"期权订单被拒绝或取消：{order.vt_orderid}")
        elif order.vt_orderid == self.underlying_orderid:
            if order.status == Status.ALLTRADED:
                self.underlying_pos += order.volume if order.direction == Direction.LONG else -order.volume
            elif order.status in [Status.REJECTED, Status.CANCELLED]:
                self.write_log(f"标的物订单被拒绝或取消：{order.vt_orderid}")

    def on_trade(self, trade: TradeData):
        """
        成交更新
        """
        if trade.vt_symbol == self.option_symbol:
            self.option_pos += trade.volume if trade.direction == Direction.LONG else -trade.volume
        elif trade.vt_symbol == self.underlying_symbol:
            self.underlying_pos += trade.volume if trade.direction == Direction.LONG else -trade.volume

    def calculate_option_price(self, tick: TickData) -> float:
        """
        计算期权理论价格
        这里需要实现具体的期权定价模型，如Black-Scholes模型
        """
        # 示例：简化的Black-Scholes模型（需要更多输入参数）
        # from scipy.stats import norm
        # d1 = (log(S/K) + (r + 0.5*sigma**2)*T) / (sigma*sqrt(T))
        # d2 = d1 - sigma*sqrt(T)
        # call = S*norm.cdf(d1) - K*exp(-r*T)*norm.cdf(d2)
        # 这里返回一个虚拟的理论价格，实际应用中需要根据具体模型计算
        return tick.last_price * 1.01

    def on_bar(self, bar: BarData):
        """
        K线数据更新
        """
        pass

    def on_stop_order(self, stop_order: StopOrder):
        """
        停止单更新
        """
        pass

    def process_exercise(self):
        """
        处理期权行权
        """
        # 检查是否到达行权日期
        # 计算是否值得行权
        # 发送行权指令
        pass

    def process_delivery(self):
        """
        处理交割
        """
        # 检查是否到达交割日期
        # 准备交割所需的资金或实物
        # 执行交割流程
        pass
